0.1 Farm Income Analysis

0.1.2 Income vs Adaptation Strategies

Income Analysis by Adaptation Strategy Adoption
adaptation_adopted count mean_current median_current sd_current mean_past income_change_amount
No 74 5426.89 4000 5805.16 20282.43 -14855.54
Yes 293 5417.46 4000 5897.17 12912.49 -7495.03

  • The findings on Income Analysis

There is a significant decline in farm incomes over the past five years among smallholder farmers in the Bono East Region.Key summary statistics show:

Current Season Income: Mean of 5,409.80 Cedis, median of 4,000.00 Cedis, standard deviation of 5,865.71 Cedis.

Income 5 Years Ago: Mean of 14,367.42 Cedis, median of 7,000.00 Cedis, standard deviation of 21,724.75 Cedis.

Overall Change: Mean decrease of 8,957.62 Cedis, representing a 62.35% decline.

The distribution of income changes indicates widespread reductions:

Decreased significantly: 153 farmers (41.6%)

Decreased slightly: 116 farmers (31.5%)

Increased significantly: 46 farmers (12.5%)

Increased slightly: 45 farmers (12.2%)

No change: 8 farmers (2.2%)

Violin and boxplot visualizations highlight a narrower and lower income distribution in the current season compared to five years ago, with most farmers reporting lower earnings. Bar plots of income change categories emphasize the dominance of decreases.

When stratified by adaptation strategy adoption:

Farmers without adaptation strategies (n=74): Mean current income 5,426.89 Cedis, mean past income 20,282.43 Cedis, mean change -14,855.54 Cedis.

Farmers with adaptation strategies (n=294): Mean current income 5,405.50 Cedis, mean past income 12,878.61 Cedis, mean change -7,473.11 Cedis.

This suggests that adopters experienced less severe income declines. Violin plots show similar median incomes (4,000 Cedis) but slightly higher variability among adopters, indicating potential benefits from adaptations in mitigating losses.

0.2 Production Costs Analysis

0.2.1 Production Cost Patterns

Production costs per acre exhibit high variability, with an overall mean of 2,039.08 Cedis, median of 1,500.00 Cedis, and standard deviation of 1,854.65 Cedis. The range spans from 200.00 to 12,000.00 Cedis, with 25th percentile at 975.00 Cedis and 75th at 2,500.00 Cedis.Breakdown by cost change and level categories (sorted by count descending).

Bar plots show that cost increases (significantly or slightly) are the most common changes, while high cost levels predominate. Violin plots by cost change category illustrate higher median costs in increasing categories, suggesting escalating expenses as a key challenge.

Scatter plots of production costs vs. current income, colored by cost change, reveal a positive linear trend (dashed regression line), indicating that higher costs often correlate with higher incomes, but with significant scatter, particularly in increasing cost groups.

Production Cost Analysis by Change and Level Categories
production_cost_change production_cost_level count mean_cost_per_acre median_cost_per_acre
Increased significantly High 145 2294.14 2000
Increased slightly High 66 2285.97 2000
Increased significantly Moderate 41 2135.34 1300
No change High 21 985.71 750
Decreased slightly High 20 1502.80 1300
Increased slightly Moderate 18 2144.44 1000
Decreased significantly High 15 1900.00 1500
Decreased slightly Moderate 14 1347.14 1000
No change Moderate 9 1277.78 800
Decreased significantly Moderate 6 1934.33 1250
Decreased slightly Low 6 1448.00 819
Decreased significantly Low 3 1866.67 1800
Increased significantly Low 2 1500.00 1500
No change Low 1 600.00 600
Production Cost per Acre - Descriptive Statistics
Statistic Value
mean_cost 2040.28
median_cost 1500.00
sd_cost 1857.04
min_cost 200.00
max_cost 12000.00
q25 950.00
q75 2500.00

0.2.2 Cost-Income Relationship

Profit margins were estimated assuming 50 Cedis per bag of yield. Summary by production cost change

Profit Margin Analysis by Production Cost Change
production_cost_change count mean_profit_margin median_profit_margin
Decreased significantly 19 31.56 84.00
Decreased slightly 26 -143.98 -17.14
Increased significantly 160 -150.10 72.50
Increased slightly 82 -68.42 66.67
No change 23 -39.03 59.09

Notably, significant cost decreases yield positive mean margins, while increases lead to negative means, though medians are often positive, indicating skewed distributions with some profitable outliers. This underscores how rising costs erode sustainability, especially without adaptations.

0.3 Soil Fertility Degradation Analysis

0.3.1 Soil Fertility Status

Distribution of Soil Fertility Change Patterns
soil_fertility_change n percentage
4 (Significant change) 152 41.4
3 (Moderate change) 69 18.8
2 (Slight change) 44 12.0
1 (No change) 16 4.4
Primary Causes of Soil Degradation (Multiple Selection)
Cause Count Percentage
Overcultivation 257 70.0
Drought 245 66.8
Erosion 245 66.8
Flooding 95 25.9
Others 15 4.1

0.3.2 Soil Fertility and Adaptation Strategies

Soil Fertility Changes by Adaptation Strategy Adoption
soil_fertility_change count_No count_Yes percentage_No percentage_Yes
1 (No change) 3 13 4.7 6.0
2 (Slight change) 2 42 3.1 19.4
3 (Moderate change) 13 56 20.3 25.8
4 (Significant change) 46 106 71.9 48.8

Soil Fertility Findings

Soil fertility changes show prevalent degradation:

Significant change: 153 farmers (41.6%)

Moderate change: 69 farmers (18.8%)

Slight change: 44 farmers (12.0%)

No change: 16 farmers (4.3%)

Primary causes (multiple selections allowed):

Overcultivation: 258 farmers (70.1%) Drought: 245 farmers (66.6%) Erosion: 245 farmers (66.6%) Flooding: 95 farmers (25.8%) Others: 15 farmers (4.1%)

Bar plots visualize the dominance of significant changes and causes like overcultivation and drought/erosion.

By adaptation adoption:

Non-adopters: Significant change (71.9%), Moderate (20.3%), Slight (3.1%), No change (4.7%).

Adopters: Significant change (49.1%), Moderate (25.7%), Slight (19.3%), No change (6.0%).

Adopters report less severe changes, suggesting adaptations mitigate degradation. Clustered bar plots show higher percentages of slight/moderate changes among adopters.

0.4 Water Availability and Food Security Analysis

0.4.1 Water Access Patterns

Distribution of Water Access Change Patterns
water_access_change n percentage
No change 116 31.6
Significant decrease 83 22.6
Moderate decrease 79 21.5
Moderate increase 55 15.0
Significant increase 34 9.3
Consistent Water Access Status
consistent_water_access n percentage
No 155 42.2
Yes 212 57.8
Irrigation Access Status
irrigation_access n percentage
No 168 45.8
Yes 199 54.2

Water Access

Water access changes indicate mixed but mostly stable or declining patterns:

No change: 116 farmers (31.5%)

Significant decrease: 83 farmers (22.6%)

Moderate decrease: 79 farmers (21.5%)

Moderate increase: 56 farmers (15.2%)

Significant increase: 34 farmers (9.2%)

Consistent water access:No (155 farmers, 42.1%), Yes (213 farmers, 57.9%). Irrigation access: No (168 farmers, 45.7%), Yes (200 farmers, 54.3%).Bar plots highlight no change as most common, with decreases nearly as prevalent. Grouped bar plots of consistency by irrigation show irrigation correlates with more consistent access.

0.4.2 Food Security Assessment

Food Security Indicators (Farmers Experiencing Issues)
Indicator Count Percentage
Preferred Foods 311 84.7
Small Meals 302 82.3
Hungry 272 74.1
Variety 259 70.6
Worry 255 69.5
Skip Meals 254 69.2
Distribution of Daily Meals
meals_per_day count percentage
3 252 68.7
2 99 27.0
4 11 3.0
5 4 1.1
1 1 0.3
Percentage of Income Spent on Food
food_expenditure_percentage n percentage
25–50% 178 48.5
51–75% 96 26.2
Less than 25% 68 18.5
More than 75% — (Ordinal) 25 6.8

0.4.3 Water-Food Security

Water Access vs Food Security Indicators
consistent_water_access irrigation_access count mean_meals
No No 118 2.82
No Yes 37 2.81
Yes No 50 2.76
Yes Yes 162 2.74

Food security indicators (farmers experiencing issues, multiple allowed):

Preferred Foods: 312 farmers (84.8%)

Small Meals: 303 farmers (82.3%) Hungry: 273 farmers (74.2%) Variety: 260 farmers (70.7%) Worry: 256 farmers (69.6%) Skip Meals: 255 farmers (69.3%)

Preferred foods 84.8% most farmers can’t always eat the foods they want. Small meals 82.3% many reduce portion sizes.

Hungry 74.2% very high hunger prevalence.

Variety 70.7 lack of diet diversity.

Worry 69.6% high anxiety about food availability.

Skip meals 69.3% frequent meal skipping.

Daily meals distribution:

3 meals: 252 farmers (68.5%)

2 meals: 99 farmers (26.9%)

4 meals: 11 farmers (3.0%)

5 meals: 4 farmers (1.1%)

1 meal: 1 farmer (0.3%)

Food expenditure as percentage of income: 25–50%: 179 farmers (48.6%) 51–75%: 96 farmers (26.1%) Less than 25%: 68 farmers (18.5%) More than 75%: 25 farmers (6.8%)

Bar plots show high prevalence of concerns like limited preferred foods and small meals, with most farmers at 3 meals/day and moderate expenditure.

Water access vs. food security:No consistent access, No irrigation: 118 farmers, mean meals 2.82

No consistent access, Yes irrigation: 37 farmers, mean meals 2.81

Yes consistent access, No irrigation: 50 farmers, mean meals 2.76

Yes consistent access, Yes irrigation: 162 farmers, mean meals 2.74

Boxplots indicate slightly higher meals with irrigation, regardless of consistency, suggesting irrigation supports better food security.

0.5 Integrated Analysis : Correlations and Relationships

0.5.1 Key Variable Correlations

Top 10 Variable Correlations
Variable1 Variable2 Correlation
avg_production_cost_per_acre income_last_season 0.246
avg_production_cost_per_acre meals_per_day -0.177
crop_area_acres_or_hectares meals_per_day -0.173
crop_area_acres_or_hectares farming_experience_years 0.114
farming_experience_years income_last_season 0.102
income_last_season meals_per_day -0.097
crop_area_acres_or_hectares current_yield_bags_or_acre 0.087
avg_production_cost_per_acre current_yield_bags_or_acre 0.085
crop_area_acres_or_hectares income_last_season 0.083
current_yield_bags_or_acre income_last_season -0.079

The strongest positive correlation is between production costs and current income (0.246), while negative links appear between costs/area and meals per day (-0.177 and -0.173), implying higher inputs/scale may strain food security. The correlation matrix plot visually reinforces these moderate relationships, with no extremely strong correlations, highlighting multifaceted influences on livelihoods. Overall, findings link climate-induced challenges ( soil/water degradation) to economic pressures, with adaptations offering partial resilience for sustainability.

0.6 Summary

0.6.1 Key Statistics Summary

Summary of Key Dimensions
Aspect Key_Finding
Farm Income Mean income (Cedis): 5419
Production Costs Mean cost per acre (Cedis): 2040
Soil Fertility 54.1% report significant decline
Water Access 42.2% lack consistent access
Food Security Mean meals per day: 2.8

1 Late Request Made

1.1 Flooding Analysis

1.1.1 Flooding Frequency and Severity Patterns

Distribution of Flooding Frequency
flooding_frequency n percentage
Never 236 64.3
Sometimes 59 16.1
Rarely 38 10.4
Often 21 5.7
Always 13 3.5
Distribution of Flooding Impact Frequency
flooding_impact_frequency n percentage
Never 230 62.7
Sometimes 59 16.1
Rarely 49 13.4
Often 17 4.6
Always 12 3.3
Distribution of Flooding Severity
flooding_severity n percentage
Low 244 66.5
Moderate 83 22.6
High 40 10.9
Changes in Flooding Incidence Over Time
flooding_incidence_change n percentage
No Change 159 43.3
Slight Increase 64 17.4
Moderate Increase 57 15.5
Very Significant Increase 47 12.8
Significant Increase 40 10.9

The analysis of flooding frequency among smallholder farmers reveals that a majority experience minimal flooding events.

The distribution is as follows:

Never: 236 farmers (64.3%)

Sometimes: 59 farmers (16.1%)

Rarely: 38 farmers (10.4%)

Often: 21 farmers (5.7%)

Always: 13 farmers (3.5%)

Bar plots illustrate “Never” as the dominant category, with frequencies tapering off toward more regular occurrences. This suggests that while flooding is not ubiquitous, a notable subset of farmers (about 35.7%) face it to varying degrees, potentially linked to climate change variability in the Bono East Region.

Flooding Impact Frequency

The frequency of flooding impacts on farming operations shows even lower prevalence, indicating that not all flooding events result in significant disruptions:

Never: 230 farmers (62.7%)

Sometimes: 59 farmers (16.1%)

Rarely: 49 farmers (13.4%)

Often: 17 farmers (4.6%)

Always: 12 farmers (3.3%)

Visualizations via bar plots highlight “Never” as the most common, with “Sometimes” and “Rarely” accounting for the bulk of occasional impacts. This implies resilience in some systems but vulnerability where impacts do occur.

Flooding Severity

When flooding does occur, its severity is generally low to moderate

Low: 244 farmers (66.5%)

Moderate: 83 farmers (22.6%)

High: 40 farmers (10.9%)

Bar plots emphasize the predominance of low severity, suggesting that while flooding happens, extreme cases are less common, possibly due to local topography or adaptive measures.

Changes in Flooding Incidence Over Time Perceptions of changes in flooding incidence over time indicate stability or mild increases:

No Change: 159 farmers (43.3%)

Slight Increase: 64 farmers (17.4%)

Moderate Increase: 57 farmers (15.5%)

Very Significant Increase: 47 farmers (12.8%)

Significant Increase: 40 farmers (10.9%)

Bar plots show “No Change” as the leading response, but over half report some increase, aligning with climate change trends of intensified hydrological events.

1.1.2 Flooding Impact on Agricultural Productivity

Yield Analysis by Flooding Frequency and Severity
flooding_frequency flooding_severity count mean_yield median_yield sd_yield
Never Low 208 1374.48 15.0 6072.37
Sometimes Moderate 28 1335.76 22.0 5980.67
Rarely Moderate 22 14285735.86 16.0 36313642.82
Never Moderate 20 802.35 27.0 2202.56
Sometimes Low 17 21201.73 602.0 28569.08
Sometimes High 14 4640.15 30.0 11115.80
Rarely Low 13 7049.62 40.0 17148.65
Often Moderate 12 46.80 54.0 16.10
Always High 8 145.00 10.0 235.56
Never High 8 155.14 16.0 249.40
Often High 7 760.50 20.0 1493.09
Always Low 4 5000.00 5000.0 0.00
Rarely High 3 16.00 16.0 NA
Often Low 2 12.50 12.5 10.61
Always Moderate 1 NaN NA NA
Income Analysis by Flooding Frequency
flooding_frequency count mean_income median_income sd_income
Never 236 5815.17 4500 5899.71
Sometimes 59 5711.41 4000 7844.24
Often 21 4857.14 5000 2471.35
Rarely 38 3540.79 3000 3479.89
Always 13 3308.08 2500 2151.30

Relationship Between Flooding and Yield

Higher frequency and severity tend to associate with erratic yields, though medians suggest more stable lower outputs in frequent flooding scenarios. This highlights flooding’s disruptive potential on productivity.

14,285,735.86, indicate data issues

Relationship Between Flooding and Income

ncome analysis by flooding frequency (sorted by mean income descending) shows an inverse relationship, with less frequent flooding linked to higher earning

Violin and boxplots depict narrower income distributions and lower medians in higher frequency categories, indicating economic vulnerability from recurrent flooding.

1.1.3 Flooding and Soil Degradation Relationship

Flooding is cited as a cause of soil degradation by 95 farmers (25.9%), while 272 (74.1%) do not attribute it as such. This moderate association suggests flooding contributes to erosion and fertility loss but is not the primary driver.

Cross-tabulation of flooding frequency vs. soil fertility changes:

Flooding as a Cause of Soil Degradation
soil_degradation_cause_flooding count percentage
0 272 74.1
1 95 25.9
Flooding Frequency vs Soil Fertility Changes
flooding_frequency n_1 (No change) n_4 (Significant change) n_2 (Slight change) n_3 (Moderate change) percentage_1 (No change) percentage_4 (Significant change) percentage_2 (Slight change) percentage_3 (Moderate change)
Always 6 5 0 0 54.5 45.5 0.0 0.0
Never 7 117 19 24 4.2 70.1 11.4 14.4
Often 1 7 4 4 6.2 43.8 25.0 25.0
Rarely 0 10 4 21 0.0 28.6 11.4 60.0
Sometimes 2 13 17 20 3.8 25.0 32.7 38.5

Clustered bar plots show higher frequencies correlate with more significant fertility changes, though “Never” has high significant change percentages, possibly due to other factors.

1.1.4 Flooding and Adaptation Strategies

Adopters are more prevalent in higher frequency categories ( 18.1% in “Sometimes” vs. 8.1% non-adopters), suggesting adaptations are responsive to flooding risks.Among adopters (n=293, but table uses 294 from prior; assuming similar), flood-related strategies:Crop Diversification: 91 farmers (31.1%) Drought Flood Resistant (varieties): 69 farmers (23.5%) Rainwater Harvesting: 26 farmers (8.9%) Agroforestry: 18 farmers (6.1%)

Bar plots and clustered visuals indicate higher adoption rates in frequent flooding groups, with crop diversification as the most common strategy for resilience.

Flooding Frequency by Adaptation Strategy Adoption
flooding_frequency n_No n_Yes percentage_No percentage_Yes
Always 3 10 4.1 3.4
Never 48 188 64.9 64.2
Often 4 17 5.4 5.8
Rarely 13 25 17.6 8.5
Sometimes 6 53 8.1 18.1
Flood-Related Adaptation Strategies Among Adopters
Strategy Count Percentage
Crop Diversification 91 31.1
Drought Flood Resistant 69 23.5
Rainwater Harvesting 26 8.9
Agroforestry 18 6.1

1.1.5 Flooding and Food Security

Higher frequencies link to lower mean meals and higher worry/skipping rates, except “Rarely” showing better outcomes.

Food Security Analysis by Flooding Frequency
flooding_frequency count mean_meals worried_about_food skipped_meals worried_percentage skipped_percentage
Always 13 2.69 11 7 84.6 53.8
Never 236 2.78 155 137 65.7 58.1
Often 21 2.76 18 13 85.7 61.9
Rarely 38 2.97 25 11 65.8 28.9
Sometimes 59 2.66 46 37 78.0 62.7
Food Expenditure Patterns by Flooding Frequency
flooding_frequency food_expenditure_percentage n percentage
Always 25–50% 2 15.4
Always 51–75% 11 84.6
Never 25–50% 117 49.6
Never 51–75% 52 22.0
Never Less than 25% 46 19.5
Never More than 75% — (Ordinal) 21 8.9
Often 25–50% 14 66.7
Often 51–75% 4 19.0
Often Less than 25% 2 9.5
Often More than 75% — (Ordinal) 1 4.8
Rarely 25–50% 14 36.8
Rarely 51–75% 13 34.2
Rarely Less than 25% 8 21.1
Rarely More than 75% — (Ordinal) 3 7.9
Sometimes 25–50% 31 52.5
Sometimes 51–75% 16 27.1
Sometimes Less than 25% 12 20.3

1.2 Integrated: Correlations

1.2.1 Key Variable Correlations

Top 15 Variable Correlations (Including Flooding Variables)
Variable1 Variable2 Correlation
flooding_freq_numeric flooding_severity_numeric 0.522
flooding_severity_numeric flooding_freq_numeric 0.522
income_last_season avg_production_cost_per_acre 0.246
avg_production_cost_per_acre income_last_season 0.246
meals_per_day flooding_freq_numeric -0.241
flooding_freq_numeric meals_per_day -0.241
crop_area_acres_or_hectares flooding_freq_numeric 0.180
flooding_freq_numeric crop_area_acres_or_hectares 0.180
avg_production_cost_per_acre meals_per_day -0.177
meals_per_day avg_production_cost_per_acre -0.177
meals_per_day crop_area_acres_or_hectares -0.173
crop_area_acres_or_hectares meals_per_day -0.173
avg_production_cost_per_acre flooding_freq_numeric 0.158
flooding_freq_numeric avg_production_cost_per_acre 0.158
farming_experience_years crop_area_acres_or_hectares 0.114

Strongest is between flooding frequency and severity (0.522), with negative ties to meals per day (-0.241) and positive to costs/area, underscoring flooding’s role in eroding sustainability. Overall, findings portray flooding as a growing threat impacting yields, incomes, soil, and food security, with adaptations like crop diversification offering mitigation, though adoption is uneven.